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11 Examples of Predictive Analytics: Industry Use Cases

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Examples of predictive analytics

By Earl Sires, Digital Content Marketer

Many industries use predictive analytics as a core part of their strategy. Modern tools make it increasingly easy to assemble your data and make improvements to your processes. Using predictive analytics has huge benefits for any organization where it’s implemented. Check out these examples of predictive analytics and how 11 industries are putting it to use!

11 Industries Using Predictive Analytics:

  • Fundraising
  • Real Estate
  • Health Care
  • Software Testing
  • Commercial A/V
  • Supply Chain Management
  • Marketing
  • Insurance
  • Food Delivery
  • Higher Education
  • Customer Service

Examples of Predictive Analytics in: Fundraising

Fundraising relies on a blend of good old-fashioned relationship building and intelligent use of data. Knowing who to connect with, and in what format to connect with them, gives you a leg up and removes costly guesswork and wasted time from the procedure.

  • Predictive modeling allows you to plan your fundraising calendar strategically. Ensure the right communications are getting to the right people at the right time.
  • Breaking your donor base down into actionable, logical segments and targeting your outreach rather than sending generic appeals will save you money on both ends of the effort and will lead to a higher success rate.
  • Fundraising data analysis also equips you to track your success rate with various fundraising efforts. This enables you to extend trends into the future to see if they are likely to continue or change.

Real Estate

Real Estate is a field where data is in demand, and one that benefits from predictive analytics tools.

  • Predictive analytics allows real estate brokers to provide projected home values to buyers. This is also a great way to assure sellers that their home is priced appropriately.
  • Real estate analytics is a major value-add that agents can offer their clients. In the age of Zillow and an overabundance of agents, it’s an easy way to distinguish yourself.
  • Applying predictive analytics to Census data allows brokerages to identify homeowners who may be interested in selling soon because of changes in life circumstances. This means outreach to potential sellers is more targeted and more effective.
  • Predictive analytics can also match ready-and-able buyers with sellers who aren’t quite ready to list their home. That extra bit of motivation may be all that’s needed to convince the seller it’s time to get their house on the market!

Healthcare and Patient Wellness

Data has been a hot topic in healthcare for several years and is a rich source of examples of predictive analytics use cases. Putting analytics to use leads to better patient outcomes, more effective treatments, and cost savings across multiple departments. That’s because predictive analytics in healthcare allows you to incorporate data from a wide variety of sources, in the hospital and outside of it.

  • By pooling data from every wing of the hospital, you can model which treatments are most likely to be effective based on that patient’s unique and comprehensive health history rather than your department’s siloed intel.
  • Incorporating outside data sources leads to even more effective and comprehensive care. In the modern, highly connected world, there is a wealth of publicly available data that can and should inform healthcare decisions. These outside factors known as Social Determinants can play a greater role in your patient’s health than anything that happens within the hospital doors. Accounting for these factors, such as behavioral data, zip code of residence, and more, allows a predictive model to tailor treatment suggestions for doctor review.
  • From an operational perspective, Social Determinant data allows a predictive model to detect the likelihood that a patient will cancel or skip an appointment, equipping administrators to line up a call list of patients to fill those slots.

Healthcare data analytics helps your organization understand the reams of data

Software Testing

Predictive analytics will improve operations throughout your entire software testing life cycle.

  • Simplify the process of analyzing vast amounts of data generated in the software testing process, by putting that data to use in modeling outcomes.
  • You can keep on top of your release schedule by monitoring timelines and using predictive modeling to determine how delays will impact the project. By identifying these issues and the reasons for them, you can course-correct in specific areas before the larger project is delayed.
  • Instantly measure customer experiences, opinions, and insights, then forecast that data to show trends, generating immediately actionable feedback to use in your design and bug-fixing process.
  • Predictive analytics can measure your customers’ mood by surveying social media and identifying trends that will allow you to get ahead of potential backlash before it gets out of hand.
  • Your test efficiency and defect detection also stand to improve when you implement predictive analytics software.

Commercial Audio/Visual

In Pro AV, data enables operational improvements in every aspect of the business.

  • Predictive analytics can open the door to new sources of revenue by highlighting market sectors you may not have been aware of.
  • Strengthen your marketing campaigns with insights on what your customers are looking for at different points in the year based on events, conferences, and functions.
  • Improve customer service by providing faster and more accurate insight into your customer’s needs and preferences.
  • Implementing predictive analytics in your Pro AV operation will give you a significant edge over competitors who are not taking advantage of the opportunity.
  • Since modern devices are “always on” and “always connected”, you can leverage a constant data stream to feed your predictive models. This means your models will be based on the most up-to-date and relevant information.

Supply Chain Management 

A poorly optimized supply chain impacts every area of your business. These examples of predictive analytics demonstrate how your supply chain can benefit from the tool.

  • The information you gather will be as up-to-date as possible, as the model can incorporate real-time data. This means all of your decisions are based on accurate, up-to-the-minute information instead of dated reports.
  • You can be much more agile in your decision-making since the model will forecast the impacts of different variables on your supply chain’s efficiency. Identifying the least efficient areas of your operation and making projections of the impact of those inefficiencies allows you to correct issues before they take effect. This will lead to a great deal of savings over time as the supply chain becomes even more efficient.
  • Model different risk factors to see how they may impact your supply chain, and incorporate information from disparate sites or sources into one model to get the most accurate, relevant picture of your operation.

Supply Chain Analytics


Marketing is another industry that operates on a vast amount of data. Metrics are used to track everything from engagement to clicks, and databases or websites store customer contact information and interaction history. With data stored in different places, marketers often cannot form a coherent, comprehensive marketing strategy and track its effectiveness, let alone predict what strategies will be most successful.

  • Predictive modeling can use a customer’s purchasing history to inform you of the most effective times to market particular products to that customer. A predictive analytics marketing strategy could be to score each prospect on how likely they are to buy, allowing you to tailor outreach based on that customer’s score.
  • It can also help you reduce customer churn by monitoring affinity for your brand, informing you of when to take action to stay in good standing.
  • This blog post illustrates these and other uses of predictive analytics in the marketing field.

Food Delivery

The food delivery industry is a rapidly growing sector ripe for data-based development. Everything is quantifiable, from delivery times and zip codes to prices and customer satisfaction. These data points can be collated and processed to improve operations and profitability while cutting down on loss.

  • GrubHub, one of the largest players in the industry, uses demographic data to make intelligent predictions about what offerings might interest a particular user, then serves that customer ads for that item.
  • DoorDash looks at relationships between variables like time of day, day of the week, and expected prep time of a food item at particular restaurants (along with other factors such as high-profile sports events and weather events) to predict the time a user will have to wait for their food.
  • Other companies use predictive analytics to plan how many drivers they will need for particular shifts and offer incentives when drivers are in demand, and they monitor customer satisfaction with particular items to estimate which food items are likely to be in high-demand soon.

Food Delivery Analytics


The insurance industry relies heavily on data: accident reports, inspections, reimbursement values, and other quantifiables. This data is critical to making the right decisions, and predictive analytics can be a great tool to make better decisions.

  • For highly complex claims with lots of factors to consider, predictive analytics can be instrumental in processing claims information and providing an informed path forward.
  • Predictive analytics can also help with fraud prevention, detecting patterns based on historical data and data from publicly available sources like social media.
  • As predictive analytics for insurance continues to evolve, it will likely start to “provide the first notice of loss handling, case reserve estimates, and initial triage, without the need for claims professional oversight,” according to Jason Rodriguez at PropertyCasualty360. You can refine and perfect these processes over time, leading to greater cost-savings and more informed rulings across the board.

Higher Education

Examples of predictive analytics in higher education include applications in enrollment managementfundraisingrecruitment, and retention. In each of these areas, predictive analytics gives a major leg up by providing intelligent insights that would otherwise be overlooked.

  • Using data from a student’s high school years, a predictive model can score each student and inform administrators on how best to support that student over the course of their enrollment.
  • Models can provide fundraisers with critical information about the best times and methods for reaching out to prospective and current donors.
  • With analytics, recruiters can more accurately target their outreach where it will lead to the greatest success at the least cost.
  • Predictive analytics can provide insight into what factors persuade students to stay at your school rather than transfer to another one.

Data analytics for higher education professionals

Customer Service

Any consumer-facing industry stands to benefit from the use of predictive analytics.

  • Marketers and sales teams can gain an understanding of when to target a particular advertisement or sales call to a customer based on their past purchasing history.
  • Predictive models give brands a clear idea of when business will be heavy and when it will be light, allowing them to staff up or down as needed.
  • It can vastly improve customer service and product feedback processes by gathering data on customer’s preferences and forecasting trends that show what service techniques and methods of incorporating feedback lead to the best results.

In Summary…

These examples of predictive analytics make clear that its applications are wide and varied. Regardless of your industry, predictive analytics can help you solve the problems you know you have and identify problems you aren’t even aware of yet. Through predictive modeling, you will gain a comprehensive and accurate understanding of your organization and how it can be improved to lead to greater future success.

The predictive analytics tools you put to use will shape your experience. Rapid Insight is the easy-to-use, brilliant-to-implement predictive analytics platform your organization is looking for. With an affordable price and unlimited free support from our expert analysts, we’ll get you up and running in no time.


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Thank you so much for sharing the predictive analytics!

Alisha Martin
1 year ago

You’re very welcome, Mohammad! We hope you continue to enjoy the content on our blog.